Figure 2 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Source publication
Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM an...
Citations
... In biometrics, different modifications and variants of LBP have also been proposed to enhance the discriminative power and robustness to distortion. Yang et al. (2012) presented personalized best bit map LBP (PBBM-LBP) for finger vein recognition system, which achieved not only better performance but also highly robust and reliable. Petpon and Srisuk (2009) suggested a new LBP operator called local line binary pattern (LLBP) for face recognition, which later on Rosdi et al. (2011) applied for finger vein recognition where experimental results show higher accuracy and low feature extraction time compared to LBP and LDP. ...
Biometric technology has drawn increasing attention and significance importance in recent years. In biometric security systems, personal identification and verification rely on their physical, behavioral, and biological characteristics. In this study, a new hand-based modality called dorsal finger creases is proposed for biometric classification. This modality is located on the dorsal surface of the finger, between the proximal knuckle and distal knuckle of the finger. However, it requires a specific feature extraction approach to extract the modality information on the selected region. Therefore, we have proposed a method for extracting the underlying features of the dorsal finger creases, called circular shift combination local binary pattern (CSC-LBP). The concept of CSC-LBP is to compute the local binary pattern within a 3×3 spatial window for each neighborhood pixel separately. Further, the concept of combination approach is applied on the individually computed eight LBP values to obtain the more discriminative feature vector. A multiclass support vector machine classifier is used for evaluating the effectiveness of the proposed CSC-LBP operator. Extensive experiments on self-collected datasets demonstrate the high classification accuracy and effectiveness of the proposed CSC-LBP method and confirm the usefulness of dorsal finger creases for personal recognition.
... Resolving the problems in the study of Lee et al. [30]; Yang et al. [28] developed a Personalized Best Bitmap (PBBM) approach that employed consistent bits. The following procedure forms a PBBM where FV samples are recorded per person. ...
In this modern era, user authentication has become vital regarding security and privacy in keeping their information and valuables safe. Password or pin-based authentication systems are insufficient to protect the security of individuals. People now anticipate more dependable and secure biometric authentication systems as a result of virtualization in both academics and industry. In this study, we discussed biometrics, various biometric authentication methods, and the work in each biometric system. We summarised and emphasised the significant contribution made by several researchers in this field. Our goal is to present a thorough analysis that may throw light on the important contributions and aid researchers looking to contribute their work to biometrics.
... Later, Rosdi et al. [9] proposed a new texture descriptor called the local line binary pattern, which shows better results than the LBP and LDP. To further improve the performance, Yang et al. proposed the personalized best bit map [10], and personalized weight maps [11] which assign different weight values for different bits according to their stability. Minutiae-based methods: The minutiae point in finger vein verification refers to bifurcation points and endpoints. ...
Due to its distinct advantages, finger vein verification has lately drawn more attention. Focusing on the characteristics of finger vein verification, construct a Siamese structure combining with a modified contrastive loss function for training the above CNN, which effectively improves the network's performance. The experimental findings demonstrate that the lightweight CNN's size shrinks to 1/6th of the pretrained-weights based CNN and that it achieves an equal error rate of 75% in the SDUMLA-HMT dataset, which outperforms cutting-edge techniques and nearly maintains the same performance as CNN that is based on pretrained weights.
... This category of the method is based on the local area, and the features extraction is in binary formation. They are local binary pattern (LBP) [52,53], the local line binary pattern (LLBP) [89], the personalized best bit maps (PBBM) [138], personalized weight maps (PWM) [12], and the local directional code (LDC) [125]. Derivation of the local binary code is made by comparing the gray level of the current pixel and its neighbors in the case of LBP and LLBP. ...
This paper discusses a comprehensive review of the previous research in the field of the finger vein recognition system with a focus on finger vein enhancements and features extraction advances and shortcomings. It starts with a general introduction of the biometric system followed by detailed descriptions on finger vein identification, and its architecture archival of it, which includes image acquisition, preprocessing of the image, feature extraction, and vein matching. This study focuses on related work proposed by previous researchers, issues in the field that originated from the related work, and a discussion of each of the issues associated and the proposed solutions to each of them. Next a comprehensive discussion on the advances and shortcomings of the existing techniques based on the qualities, capturing device, database, and feature of that quality is presented. Accurate comparisons between existing techniques are presented as tables to make it easy for new researchers to come up with advances and drawbacks of each technique without spending time on all existing research in this area.
... This can help educators tailor their teaching and improve student learning. In addition, these types of systems can help identify long-term patterns of behavior and emotions, which can help educators develop more effective interventions to help students learn [14]. ...
Educational models currently integrate a variety of technologies and computer applications that seek to improve learning environments. With this objective, information technologies have increasingly adapted to assume the role of educational assistants that support the teacher, the students, and the areas enrolled in educational quality. One of the technologies that are gaining strength in the academic field is computer vision, which is used to monitor and identify the state of mind of students during the teaching of a subject. To do this, machine learning algorithms monitor student gestures and classify them to identify the emotions they convey in a teaching environment. These systems allow the evaluation of emotional aspects, based on two main elements, the first is the generation of an image database with the emotions generated in a learning environment such as interest, commitment, boredom, concentration, relaxation, and enthusiasm. The second is an emotion recognition system, through the recognition of facial gestures using non-invasive techniques. This work applies techniques for the recognition and processing of facial gestures and the classification of emotions focused on learning. This system helps the tutor in a modality of face-to-face education and allows him to evaluate emotional aspects and not only cognitive ones. This arises from the need to create a base of images focused on the spontaneous learning of emotions since most of the works reviewed focus on these acted-out emotions.
... It's possible that freezing the earliest layers may yield characteristics that can be applied to a wide range of jobs. The work may produce even more specific characteristics in our datasets by fine-tuning the next layers [34][35][36][37][38][39][40]. ...
Due to their combination of security and economic viability, finger vein biometrics have gained considerable traction in recent years. They have the advantage of being the least vulnerable to identity theft because veins are present beneath the skin, as well as being unaffected by the ageing process of the user. To address the ever-increasing need for security, all of these variables necessitate working models. Using face recognition and AI-based biomet-rics has become a hot subject in law enforcement because of the accidental demographic bias it introduces into the process. Biometric prejudice, on the other hand, has far-reaching implications that transcend into everyday situations. When an ATM transaction or an online banking transaction is compromised by a fake positive or negative verification, it makes it simpler for fraudsters to carry out their criminal activities. The veins of a fingertip were the subject of this research project's investigation. Deep convolutional neural network models were utilised to extract features from two widely-used and freely-available da-tasets of finger veins. Finger vein identification as a unique biometric approach has received a lot of attention recently. Accuracy of greater than 98 percent is reached with the deployment of multi-class categorization. The binary classification based model has a 97.51 percent accuracy rate. The total outcomes and their effectiveness are fairly good with the implementation situations. Deep learning, an end-to-end technique that has demonstrated promising results in domains like face recognition and target detection, may be useful for finger vein recognition.
... While Monte Carlo (MC) assessment of light transport through the skin layers and into the blood vessel was assessed in early research [9] , Boles and Chu (1997) [10] illustrated the possibility of personal authentication using images of the human palm. This initiated research in both the biomedical [4,[11][12][13] and biometric [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] domains. The current research draws inspiration from biometric identification methods and extends it to identification and monitoring of blood vessels for non-contact heart rate measurement. ...
... These methods suffered from interference from ambient conditions and human body conditions when using the far-infrared wavelengths, while the near-infrared transmission was inconsistent due to skin and hair attenuation [14] . The transmissive arrangement was used extensively [16][17][18]23,26] while other methods attempted to use data fusion combining visible and near-infrared transmission images for imaging the veins and the dorsal texture of the skin for identification [21] or combining reflectance and transmission near-infrared data [24] . A key review article in this domain was presented by Hashimoto (2006) mentioning the advantages of non-invasive imaging, safety from forgery or theft due to the veins being hidden under the surface and the stability of vasculature allowing simplistic methods to capture these patterns [15] . ...
... Of the many algorithms, a choice was made to apply two methods: maximum curvature from image profiles [30] and repeated line tracking [31] . The choice was supported by the high degrees of accuracy when compared to other algorithms used for similar biometric analyses [18,19] . ...
Today's diagnostics include devices such as pulse oximeters, blood pressure monitors, and temperature measurements. These devices provide vital information to medical personnel when making treatment decisions. Drawing inspiration from the fundamental utility of pulse oximeters, we present a methodology for a robust low-cost approach to imaging subsurface vasculature and monitoring heart rate. The approach uses off-the-shelf equipment, set up in free space without physical contact and exploits the nature of the interaction between light at near-infrared wavelengths with tissue. Image processing algorithms extract heart rate information from the snapshot and video sequence captured at a stand-off distance. The method can be applied in a room with ambient light and remains robust to scenarios comparable to medical situations. This research sets the platform for future diagnostic devices based on imaging systems and algorithms for non-contact point-of-care investigations.
... It's possible that freezing the earliest layers may yield characteristics that can be applied to a wide range of jobs. The work may produce even more specific characteristics in our datasets by fine-tuning the next layers [34][35][36][37][38][39][40]. ...
Finger vein biometrics have gained a lot of attention in recent years because they offer the perfect balance of security and economic viability, with advantages such as being the least susceptible to identity theft because veins are present beneath the skin, being unaffected by ageing of the person, etc. All of these factors make it necessary to create functioning models to meet the ever-increasing need for security. The use of facial recognition and AI-based biometrics, particularly in law enforcement, has become a hot topic because of its inadvertent demographic bias. Biometric bias, on the other hand, has far-reaching consequences that extend into daily use cases. When an ATM transaction or an online banking transaction is compromised by a false positive or negative verification, fraudulent activity is made easier. The study in this research work focused on the difficulty of determining the veins of a fingertip. On two widely used and freely available datasets of finger veins, we applied deep convolutional neural network models to feature extraction. Finger vein recognition has gotten a lot of interest recently as a novel biometric technique. Finger vein recognition might benefit from applying deep learning, an end-to-end approach that has shown promising results in sectors like face recognition and target detection.
... In the field of finger vein, there are also many methods based on LBP [27]. For instance, Yang et al. [28] proposed to use personalized best bit mapping based on LBP. Experiments showed that this method has better performance and relia-Signal, Image and Video Processing bility. ...
The texture of finger veins is distributed in a network structure, which can be described as regional texture feature. In the image preprocessing stage, noise generated by segmentation algorithm will lead to the loss of texture structure information. Local binary pattern (LBP) feature extraction, which does not require segmentation of images, can effectively reveal local texture features and is robust to monotonic changes in grayscale. However, the LBP operator has two obvious shortcomings: (1) the microscopic limitation: it is easy to lose local information; (2) the feature unity: it will lead to the loss of other feature information. To tack these problems, this paper proposes a multi-feature partitioned local binary pattern (MFPLBP) operator for finger vein recognition. The concept of multi-feature partition is employed to extend the traditional LBP operator. Through the partition processing of the finger vein feature image, the global and local grasp of the image is enhanced, and the influence of local noise on the overall recognition accuracy is weakened. Additionally, the idea of multi-feature fusion is used to make up for the singleness of traditional algorithms. In image recognition, the histogram cross-check is used to judge the similarity of the vein feature histogram. Finally, the experiment showed that the recognition rate of this method has increased by about 13% compared with LBP, and it has increased by about 2% compared with partitioned local binary pattern (PLBP) and traditional multi-scale LBP.
... Zhang et al. [31] presented directional binary code, which is a new LBP variant. Yang et al. [32] suggested to use LBP-based personalized best bit mapping. Experimental results show that the method not only has better accuracy, but also has higher reliability and robustness. ...
Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tackle these problems, this paper proposes a new finger vein recognition algorithm by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate, and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then, the centrosymmetric coding was performed using the matrix generated by blocking and averaging. The obtained codewords were concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-the-art methods and has competitive potential in performing the matching task.
